Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Introduction to network analysis in systems biology.

ABSTRACT: This Teaching Resource provides lecture notes, slides, and a problem set for a set of three lectures from a course entitled "Systems Biology: Biomedical Modeling." The materials are from three separate lectures introducing applications of graph theory and network analysis in systems biology. The first lecture describes different types of intracellular networks, methods for constructing biological networks, and different types of graphs used to represent regulatory intracellular networks. The second lecture surveys milestones and key concepts in network analysis by introducing topological measures, random networks, growing network models, and topological observations from molecular biological systems abstracted to networks. The third lecture discusses methods for analyzing lists of genes and experimental data in the context of prior knowledge networks to make predictions.

SUBMITTER: Ma'ayan A

PROVIDER: S-EPMC3196357 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Introduction to network analysis in systems biology.

Ma'ayan Avi A

Science signaling 20110906 190

This Teaching Resource provides lecture notes, slides, and a problem set for a set of three lectures from a course entitled "Systems Biology: Biomedical Modeling." The materials are from three separate lectures introducing applications of graph theory and network analysis in systems biology. The first lecture describes different types of intracellular networks, methods for constructing biological networks, and different types of graphs used to represent regulatory intracellular networks. The sec ...[more]

PMID: 21917719

Similar Datasets

Integrated network analysis and effective tools in plant systems biology.

Project description:One of the ultimate goals in plant systems biology is to elucidate the genotype-phenotype relationship in plant cellular systems. Integrated network analysis that combines omics data with mathematical models has received particular attention. Here we focus on the latest cutting-edge computational advances that facilitate their combination. We highlight (1) network visualization tools, (2) pathway analyses, (3) genome-scale metabolic reconstruction, and (4) the integration of high-throughput experimental data and mathematical models. Multi-omics data that contain the genome, transcriptome, proteome, and metabolome and mathematical models are expected to integrate and expand our knowledge of complex plant metabolisms.

| S-EPMC4219401 | biostudies-literature

Logic-based models in systems biology: a predictive and parameter-free network analysis method.

Project description:Highly complex molecular networks, which play fundamental roles in almost all cellular processes, are known to be dysregulated in a number of diseases, most notably in cancer. As a consequence, there is a critical need to develop practical methodologies for constructing and analysing molecular networks at a systems level. Mathematical models built with continuous differential equations are an ideal methodology because they can provide a detailed picture of a network's dynamics. To be predictive, however, differential equation models require that numerous parameters be known a priori and this information is almost never available. An alternative dynamical approach is the use of discrete logic-based models that can provide a good approximation of the qualitative behaviour of a biochemical system without the burden of a large parameter space. Despite their advantages, there remains significant resistance to the use of logic-based models in biology. Here, we address some common concerns and provide a brief tutorial on the use of logic-based models, which we motivate with biological examples.

| S-EPMC3612358 | biostudies-literature

Facile: a command-line network compiler for systems biology.

Project description:BackgroundA goal of systems biology is the quantitative modelling of biochemical networks. Yet for many biochemical systems, parameter values and even the existence of interactions between some chemical species are unknown. It is therefore important to be able to easily investigate the effects of adding or removing reactions and to easily perform a bifurcation analysis, which shows the qualitative dynamics of a model for a range of parameter values.ResultsWe present Facile, a Perl command-line tool for analysing the dynamics of a systems biology model. Facile implements the law of mass action to automatically compile a biochemical network (written as, for example, E + S <-> C) into scripts for analytical analysis (Mathematica and Maple), for simulation (XPP and Matlab), and for bifurcation analysis (AUTO). Facile automatically identifies mass conservations and generates the reduced form of a model with the minimum number of independent variables. This form is essential for bifurcation analysis, and Facile produces a C version of the reduced model for AUTO.ConclusionFacile is a simple, yet powerful, tool that greatly accelerates analysis of the dynamics of a biochemical network. By acting at the command-line and because of its intuitive, text-based input, Facile is quick to learn and can be incorporated into larger programs or into automated tasks.

| S-EPMC1976619 | biostudies-literature

A systems biology approach to pathogenesis of gastric cancer: gene network modeling and pathway analysis.

Project description:BackgroundGastric cancer (GC) ranks among the most common malignancies worldwide. This study aimed to find critical genes/pathways in GC pathogenesis.MethodsGene interactions were analyzed, and the protein-protein interaction network was drawn. Then enrichment analysis of the hub genes was performed and network cluster analysis and promoter analysis of the hub genes were done. Age/sex analysis was done on the identified genes.ResultsEleven hub genes in GC were identified in the current study (ATP5A1, ATP5B, ATP5D, MT-ATP8, COX7A2, COX6C, ND4, ND6, NDUFS3, RPL8, and RPS16), mostly involved in mitochondrial functions. There was no report on the ATP5D, ND6, NDUFS3, RPL8, and RPS16 in GC. Our results showed that the most affected processes in GC are the metabolic processes, and the oxidative phosphorylation pathway was considerably enriched which showed the significance of mitochondria in GC pathogenesis. Most of the affected pathways in GC were also involved in neurodegenerative diseases. Promoter analysis showed that negative regulation of signal transduction might play an important role in GC pathogenesis. In the analysis of the basal expression pattern of the selected genes whose basal expression presented a change during the age, we found that a change in age may be an indicator of changes in disease insurgence and/or progression at different ages.ConclusionsThese results might open up new insights into GC pathogenesis. The identified genes might be novel diagnostic/prognostic biomarkers or potential therapeutic targets for GC. This work, being based on bioinformatics analysis act as a hypothesis generator that requires further clinical validation.

| S-EPMC10364406 | biostudies-literature

Topological sensitivity analysis for systems biology.

Project description:Mathematical models of natural systems are abstractions of much more complicated processes. Developing informative and realistic models of such systems typically involves suitable statistical inference methods, domain expertise, and a modicum of luck. Except for cases where physical principles provide sufficient guidance, it will also be generally possible to come up with a large number of potential models that are compatible with a given natural system and any finite amount of data generated from experiments on that system. Here we develop a computational framework to systematically evaluate potentially vast sets of candidate differential equation models in light of experimental and prior knowledge about biological systems. This topological sensitivity analysis enables us to evaluate quantitatively the dependence of model inferences and predictions on the assumed model structures. Failure to consider the impact of structural uncertainty introduces biases into the analysis and potentially gives rise to misleading conclusions.

| S-EPMC4284538 | biostudies-literature

Mouse behavioural analysis in systems biology.

Project description:Molecular techniques allowing in vivo modulation of gene expression have provided unique opportunities and challenges for behavioural studies aimed at understanding the function of particular genes or biological systems under physiological or pathological conditions. Although various animal models are available, the laboratory mouse (Mus musculus) has unique features and is therefore a preferred animal model. The mouse shares a remarkable genetic resemblance and aspects of behaviour with humans. In this review, first we describe common mouse models for behavioural analyses. As both genetic and environmental factors influence behavioural performance and need to be carefully evaluated in behavioural experiments, considerations for designing and interpretations of these experiments are subsequently discussed. Finally, common behavioural tests used to assess brain function are reviewed, and it is illustrated how behavioural tests are used to increase our understanding of the role of histaminergic neurotransmission in brain function.

| S-EPMC1180709 | biostudies-literature

Toward a systems biology of mouse inner ear organogenesis: gene expression pathways, patterns and network analysis.

Project description:We describe the most comprehensive study to date on gene expression during mouse inner ear (IE) organogenesis. Samples were microdissected from mouse embryos at E9-E15 in half-day intervals, a period that spans all of IE organogenesis. These included separate dissections of all discernible IE substructures such as the cochlea, utricle, and saccule. All samples were analyzed on high density expression microarrays under strict statistical filters. Extensive confirmatory tests were performed, including RNA in situ hybridizations. More than 5000 genes significantly varied in expression according to developmental stage, tissue, or both and defined 28 distinct expression patterns. For example, upregulation of 315 genes provided a clear-cut "signature" of early events in IE specification. Additional, clear-cut, gene expression signatures marked specific structures such as the cochlea, utricle, or saccule throughout late IE development. Pathway analysis identified 53 signaling cascades enriched within the 28 patterns. Many novel pathways, not previously implicated in IE development, including beta-adrenergic, amyloid, estrogen receptor, circadian rhythm, and immune system pathways, were identified. Finally, we identified positional candidate genes in 54 uncloned nonsyndromic human deafness intervals. This detailed analysis provides many new insights into the spatial and temporal genetic specification of this complex organ system.

| S-EPMC2013721 | biostudies-literature

Single cell protein analysis for systems biology.

Project description:The cellular abundance of proteins can vary even between isogenic single cells. This variability between single-cell protein levels can have regulatory roles, such as controlling cell fate during apoptosis induction or the proliferation/quiescence decision. Here, we review examples connecting protein levels and their dynamics in single cells to cellular functions. Such findings were made possible by the introduction of antibodies, and subsequently fluorescent proteins, for tracking protein levels in single cells. However, in heterogeneous cell populations, such as tumors or differentiating stem cells, cellular decisions are controlled by hundreds, even thousands of proteins acting in concert. Characterizing such complex systems demands measurements of thousands of proteins across thousands of single cells. This demand has inspired the development of new methods for single-cell protein analysis, and we discuss their trade-offs, with an emphasis on their specificity and coverage. We finish by highlighting the potential of emerging mass-spec methods to enable systems-level measurement of single-cell proteomes with unprecedented coverage and specificity. Combining such methods with methods for quantitating the transcriptomes and metabolomes of single cells will provide essential data for advancing quantitative systems biology.

| S-EPMC6204083 | biostudies-literature

Systems biology approach to identify gene network signatures for colorectal cancer.

Project description:In this work, we integrated prior knowledge from gene signatures and protein interactions with gene set enrichment analysis (GSEA), and gene/protein network modeling together to identify gene network signatures from gene expression microarray data. We demonstrated how to apply this approach into discovering gene network signatures for colorectal cancer (CRC) from microarray datasets. First, we used GSEA to analyze the microarray data through enriching differential genes in different CRC-related gene sets from two publicly available up-to-date gene set databases - Molecular Signatures Database (MSigDB) and Gene Signatures Database (GeneSigDB). Second, we compared the enriched gene sets through enrichment score, false-discovery rate, and nominal p-value. Third, we constructed an integrated protein-protein interaction (PPI) network through connecting these enriched genes by high-quality interactions from a human annotated and predicted protein interaction database, with a confidence score labeled for each interaction. Finally, we mapped differential gene expressions onto the constructed network to build a comprehensive network model containing visualized transcriptome and proteome data. The results show that although MSigDB has more CRC-relevant gene sets than GeneSigDB, the integrated PPI network connecting the enriched genes from both MSigDB and GeneSigDB can provide a more complete view for discovering gene network signatures. We also found several important sub-network signatures for CRC, such as TP53 sub-network, PCNA sub-network, and IL8 sub-network, corresponding to apoptosis, DNA repair, and immune response, respectively.

| S-EPMC3354560 | biostudies-literature

A General Introduction to Glucocorticoid Biology.

Project description:Glucocorticoids (GCs) are steroid hormones widely used for the treatment of inflammation, autoimmune diseases, and cancer. To exert their broad physiological and therapeutic effects, GCs bind to the GC receptor (GR) which belongs to the nuclear receptor superfamily of transcription factors. Despite their success, GCs are hindered by the occurrence of side effects and glucocorticoid resistance (GCR). Increased knowledge on GC and GR biology together with a better understanding of the molecular mechanisms underlying the GC side effects and GCR are necessary for improved GC therapy development. We here provide a general overview on the current insights in GC biology with a focus on GC synthesis, regulation and physiology, role in inflammation inhibition, and on GR function and plasticity. Furthermore, novel and selective therapeutic strategies are proposed based on recently recognized distinct molecular mechanisms of the GR. We will explain the SEDIGRAM concept, which was launched based on our research results.

| S-EPMC6621919 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data